Ever typed a search query, only to get frustratingly irrelevant results? You’re not alone.
Eighty-four percent of companies fail to optimize their search experience, leading to lost productivity, missed sales, and poor digital experiences. For those who do, an often overlooked area for optimization is where it all begins — which is the user search query.
Search, aka information retrieval, is crucial to today’s enterprise digital experiences, by helping users navigate complex and crowded websites for needed information. Up to 30% of visitors to a site use the search box, and visitors performing an on-site search convert at 1.8 times the rate of the websites’ average, according to Econsultancy. Whether this means customers browsing for products, employees looking up organizational policies, or partners retrieving product information, enterprise search can make or break the digital journey.
Traditionally, search queries — the set of words a user types into the search box — determined how quickly and accurately an individual found their needed information. With modern information retrieval systems, companies have tools and techniques at their disposal that improve search queries on the backend. Ultimately, this leads to more satisfied users and greater engagement across digital platforms.
In this article, we explore what makes up search query optimization and best practices for enterprises to improve queries for a well-running information retrieval system.
What Is A Search Query?
A search query, sometimes also referred to as a keyword, is the set of words a user types into a database or search engine to request specific information. It’s a representation of the user’s search intent. A query can range from a single word or phrase to more complex queries making use of Boolean operators such as “and.”
In the information retrieval process, a query is sent to the search system for comparison against a collection of documents (i.e., the search index), from which the system retrieves the most relevant results and ranks them for the user.
Gaining a deeper understanding of queries and the intent behind them benefits businesses by improving search quality and returning better results for their users.
There are four main kinds of queries:
- Informational: What is AI-powered search?
- Commercial: Coveo vs. Elastic: Which AI search platform is better?
- Transactional: Buy enterprise search software
- Navigational: Coveo case studies
Search queries can be challenging to formulate. The process is rife with opportunities for misunderstandings between the user and the system.
Challenges include spelling errors, ambiguity in word meanings, synonym variations, vagueness, lack of domain knowledge or not understanding how to use site operators to filter results such as with “site.com” or Boolean operators.
When employees can’t find the right internal policies due to vague queries, productivity plummets, and frustration increases. When customers search for products using incorrect terminology, conversions drop.
Key Principles of Search Query Optimization
Despite the challenges, there are tools and techniques enterprises can adopt to support better query formulation.
Search query optimization is the process of refining a query to produce more precise and relevant results from a search. Optimizing the way a search is conducted improves the speed, quality and accuracy of retrieving information across an organization. Search query optimization also leads to helpful and satisfying user experiences.
There are a few key principles organizations should follow for achieving success in search query optimization:
Use Natural Language Processing (NLP) and AI to Enhance Query Understanding
Traditional search engines struggle with query ambiguity, failing to grasp context. AI-driven search solutions bridge this gap by leveraging NLP to interpret meaning, rather than just matching keywords.
NLP uses the patterns and contextual relationships between words to decipher the way humans speak, making searches more intuitive, fast and accurate. Helpfully, a user doesn’t have to use exact phrasing for the system to understand the meaning behind the query.
For example, a search for “open coffee shops” will bring up businesses that are still open and located within a few miles of the user because the system recognizes the intent to go to the shop.
Understand Context and Intent in Query Formulation
Search intent is the why behind writing a query. Is the user looking for general information, something specific or an action to take? Understanding the user’s intent and the context, such as the user’s profile and past actions, is critical to supporting optimal query formulation so that the system adapts to providing search results that match the need as much as possible.
As touched on above, advances in AI have made it possible for modern information retrieval systems to understand context and intent, leading to better results than simply matching keywords.
Offer Techniques for Refining Search Queries for Better Results
Enterprises can also put the power of search result refinement in the hands of their users, by offering features like:
- Develop relevant categorization that helps users narrow results such as date, location, price, or category.
- Apply autocomplete and query suggestionsthat assists users in forming a query based on factors such as popular searches and the user’s search history.
- Further refine with faceted search that adds relevant, detailed options for the user to reduce the search results (e.g., a clothing retailer giving options to select certain brands or materials).
Tools and Technologies for Query Optimization
Search technologies have advanced with AI breakthroughs and maturing of the enterprise search market to have a transformative impact on query optimization.
AI-Driven Enterprise Search Technologies
AI-powered enterprise search has revolutionized the way queries are handled, allowing users to search for information across internal and external company sources regardless of format. Users can use natural language that is understood by the search system without the need for precise wording and terminology.
These innovations have led to more intuitive, personalized and precise search experiences for users. In query optimization, there are a number of ways AI is factoring into improving enterprise search:
Contextual understanding
AI brings a semantic approach to querying and information retrieval, capturing the meaning of queries and content. Using NLP to interpret human language, search engines understand the context and intent behind a query.
In practice this means AI optimizes and enriches queries, particularly ones that are complex, ambiguous or pull from very large amounts of data. It does this by distinguishing word meanings using context, such as determining in a query that “advice” is misspelled as “advise.”
This is a significant advancement from keyword-based searches that depend on matching keywords in the query to documents, and often miss important nuances such as processing synonyms and related terms.
Relevant reading: 7 Ways to Avoid “No Search Results Found” Pages On Your Site
Personalized recommendations
With machine learning (ML) algorithms,AI search tailors search by analyzing and continuously learning from data like user profile, behavior, history, and contextual data such as time of day and location. In query optimization, this means the search engine enhances the query with information specific to the user to surface accurate results.
Predictive search
Using ML, AI-powered search refines queries using previous interactions and data on successful searches from other users to predict what the user is likely searching for. The search system in real time offers possible completions to the query, alternate suggestions or even results while the user is typing, saving time and improving efficiency.
Generative AI
It feels like it goes without saying that search experiences are fundamentally changing, so a blog on search would be remiss to not discuss generative AI’s impact. From Google’s AI Overviews to the many large language models designed to help searchers parse information faster, generative AI is another AI model to add to your enterprise toolkit when designing your customer or employee experience.
When grounded in trusted enterprise content, generative AI can help connect searchers with the answers they’re looking for, rather than just a list of links. GenAI offers a conversational experience that transforms search from merely inputting a keyword to being able to ask a question. It can synthesize information from across multiple documents, taking the burden of evaluating relevancy off of the searcher.
Out-of-the-Box vs. Build-It-Yourself
When looking to optimize search, companies face the decision of either building a solution themselves or investing in an out-of-the-box technology. Organizations with specific requirements and need for comprehensive control of the search system could benefit from building their own solution. The most popular ways to accomplish this today include using mature, open-source technologies such as Elasticsearch or Apache Solr, both enterprise-level search engines built on Apache Lucene.
Of course, this route will require an organization to possess a robust team of programmers to build, implement and maintain the search solution.
For companies looking for a shorter timeline and an easier to maintain solution with advanced, pre-built features, they should investigate an out-of-the-box search platform. Today’s advanced enterprise search solutions come with many features for optimizing search queries using machine learning algorithms and natural language processing.
Coveo, for example, provides an AI-powered search platform with real-time query recommendations, personalized suggestions based on the user behavior and profile, and dynamic filtering for content that’s relevant to the query. Its platform also provides a solid foundation for generative capabilities.
Ultimately, the best decision will come down to the company’s specific use cases, timeline, resources and organization-wide needs.
Relevant reading: Free Infographic | Should You Build or Buy AI Search?
Best Practices for Improving Information Retrieval
Besides deciding on the right search technologies, organizations can increase their chances for successful query optimizations with these best practices.
- Leverage user data and behavior analytics: Tracking patterns in search behavior and how users formulate their queries can help companies identify areas for improvement, such as accounting for popular spelling errors and content or product gaps.
- Implement advanced search filters and facets: Businesses that offer facets and filters help users navigate information quickly — especially if their query is broad or vague.
For example, facets let users narrow results; an initial query of “men’s boots” can be further refined with size, color or shoe type. In an enterprise setting, employees may benefit from additional filtering of documents or people by author, file type, department, or location.
- Enhance search with synonyms and related terms: Define relationships between common synonyms and related terms for your business to ensure your users find what they are looking for without using the exact term, such as creating a rule associating the word “sneakers” with “running shoes” and “trainers.”
- Continuously test and optimize strategies: Monitoring user searches and their results allows your business to move forward with improving searches and doing away with irrelevant or confusing results that can turn away potential customers or frustrate employees.
Case Studies
Many companies across industries and use cases have realized significant benefits for their businesses through search query optimization. Nielsen Norman Group has found that employee search suggestions greatly reduce the amount of time organizations spend on search, potentially saving companies hundreds of thousands of dollars per year
Let’s take a look at a few real-world examples of search optimization in action.
Ecommerce
To meet rising customer expectations for an omnichannel experience, Freedom Furniture sought to transform its ecommerce experience with a focus on search and product discovery. They knew they needed an AI-powered solution to support both its growth and ensure a seamless expeirence.
They chose Coveo for its technology roadmap, offering a clear vision for future innovation like generative AI. Today, Coveo’s AI-Relevance Platform is already transforming the Freedom Furniture shopping experience, achieving 5.5% uplift in average order value, a 15% increase in engagement with search, and a 50% reduction in merchandising effort.
Customer Service
Born in the cloud, Xero, a worldwide market leader in accounting software, has every answer to every support question that’s ever been asked. In 2017, Xero partnered with Coveo to make that wealth of information available to customers via self-service. Taking advantage of Coveo’s out-of-the-box features that enhanced search queries, Xero started first by empowering internal employees with intelligent search, and then extending that to external customers.
When generative AI appeared, Xero’s partnership with Coveo allowed them to roll out the technology and start reaping its benefits in just 6 weeks. In that short time, Xero achieved a 20% improvement in self-service success.
Workplace Efficiency
Comcast, a leading telecom and entertainment provider, has embraced the promise – and the challenges – of AI head on. They needed to deploy AI in a way that made sense for an enterprise with more than 100,000 employees and contractors (safety and governance were key). They were also tasked with backend reconfiguring to align tools in a way that made it easier to create, share, manage, and retrieve content.
By partnering with Rightpoint and Coveo to implement an entirely new AI-driven approach to knowledge sharing and content management, Comcast has seen significant improvements with how employees interact with content. Content sharing skyrocketed by 92%, while overall content consumption and engagement increasing 60%. Video viewership also saw a dramatic 92% boost, indicating that employees are finding and utilizing resources more effectively.
Future Trends in Search Query Optimization
Rapid advances in AI, machine learning and natural language processing are still developing and taking shape, impacting trends in the field of information retrieval and search query optimization.
Emerging technologies and innovations in search include voice search along with natural language generation, allowing for conversational interactions between users and search systems. Additionally, multimodal searches are on the rise with AI-powered search providing the ability to search across text, images, video and audio, opening up new avenues of interaction and forms of querying. Organizations will need to prepare their search systems to handle various types of queries in the future to continue providing accurate and satisfying search experiences.
Additionally, the search query experience is changing to become more predictive and personalized. Search systems, such as those underpinning generative AI applications, will get better at anticipating what users are looking for before they even finish typing (or speaking).
As AI collects more information about a user and improves its analytics, greater personalization will come to search queries. This also provides businesses with more opportunities to engage potential customers with custom offers and optimal timing, leading to greater engagement.
Conclusion
An effective information retrieval system directly impacts an enterprise’s decision making, employee productivity, and competitiveness. Search query optimization is an important way to improve the communication between the user and the search system, leading to better search results and satisfying experiences for the user.
While queries are challenging to search systems due to factors like ambiguity and errors, advances in AI search technologies are overriding many of these obstacles and equipping enterprises with the ability to optimize queries, more deeply understand their users and drive better business outcomes.
Contact Coveo to learn how our advanced AI-driven search solutions can optimize your search queries and significantly improve information retrieval across your organization.
Dig Deeper
Curious about how to best evaluate the different information retrieval systems on the market? Snag a free copy of The Buyers Guide for Information Retrieval Systems, which breaks down the key components for a competitive digital experience, such as the difference between enrichment at index and enrichment at query time.